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BUHAY DENTISTA > MMW FINALS > Flashcards

MMW FINALS Flashcards

1
Q

Arrangement of raw data into class
intervals and frequency

A

Frequency Distribution Table

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2
Q

either nominal or ordinal level of
measurement

A

Qualitative Data

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3
Q

Vertical bars that have no gaps
because of class boundaries

A

Histogram

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4
Q

Five-number summary

Minimum, lower quartile, median, upper quartile,
maximum

A

Box-and-Whisker Plot

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5
Q

Used when there are extreme values in the dataset,

A

Outliers

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6
Q

utliers lie beyond the ranges of values and can be determined by using:

A

lower quartile – 1.5IQR
upper quartile + 1.5IQR

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7
Q

presents the score values and their frequency of occurrence. When presented in a table, the score values are listed in rank order, with the lowest score value usually at the bottom of the table.

A

frequency distribution

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8
Q

The steps for constructing a frequency
distribution of grouped scores are as
follows:

A
  1. Find the range of the scores.
    Range = Highest Score – Lowest Score
  2. Determine the tentative number of classes (K).
    𝐾 =1+[3.322(log 𝑁 )]
  3. Determine the width of each class interval (i).
    𝑖= 𝑅
    𝐾
  4. List the interval, placing the interval containing the lowest score
    value at the bottom.
  5. Tally the raw scores into the appropriate class intervals.
  6. Add the tallies for each interval to obtain the interval frequency.
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9
Q

Displays data by using bars of equal
width on a grid. The bars may be vertical or horizontal. Bar graphs are used for comparisons.

Displays a bar for each category with
the length of each bar representing the frequency of that category.

A

Bar Graph

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10
Q

ordered from
highest to lowest
frequency.

A

A Pareto Chart

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11
Q

to show how data represent
portions of one whole or
one group.

A

Circle Graph (Pie Chart)

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12
Q

Notice that each sector is
represented by %

A

Circle Graph (Pie Chart)

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13
Q

joined by line segments to
show trends over
time.

A

Broken Line Graph

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14
Q

which points on
the line between the plotted
points also have meaning.
Sometimes, this is a “best fit”
graph where a straight line is
drawn to fit the data points.

A

Continuous Line Graph

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15
Q

Notice that the
independent variable is
on the x-axis, & the
dependent is on the y-
axis.

A

Continuous Line Graph

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16
Q

Uses pictures and
symbols to display data;
each picture or symbol
can represent more than
one object; a key tells
what each picture
represents.

A

Pictograph

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17
Q

A graph of data that is a set of points.

A

Scatter Plot

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18
Q

IQR (Interquartile Range)

A

Q1 – Lower Quartile
Q3 – Upper Quartile

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19
Q

A value that “lies outside” (is much smaller or larger than) most of the other values in a set of data

A

outlier

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20
Q

One way to determine if a data point is an outlier is to use the interquartile range (IQR) method.

A

Lower Boundary : Q1 – 1.5 IQR
Upper Boundary : Q3 + 1.5 IQR

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21
Q

formula Variance

A

S2 =
Where:
x – scores
- mean
n – number of samples

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22
Q

formula Standard Deviation

A

S =
Where:
x – scores
- mean
n – number of samples

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23
Q

The variance can be found by following these four steps

A
  1. Find the mean.
    2.Subtract the mean from
    each of the five
    samples/observations.
  2. Squaring these deviations
    from the mean
  3. Taking the average of these
    squared deviations.
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24
Q

These are unit-less and are used
when one wishes to compare
the scatter of one distribution
with another distribution.

A

Measures of Relative Dispersion

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25
Q

It measures how many standard
deviation is above or below the
mean.

A

Standard Score

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26
Q

It is computed as
and the sample counterpart is

A

Standard Score

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27
Q

occurs when the values of variables appear at regular frequencies and often the mean, median, and mode all occur at the same point. If a line were drawn dissecting the middle of the graph, it would reveal two sides that
mirror one other.

A

symmetrical distribution

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28
Q

lack of symmetry
can be right skewed distribution or left skewed distribution

A

asymmetric distribution

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29
Q

is a measure or a criterion on how asymmetric the distribution of data is from the mean.

A

Skewness

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30
Q

is a method developed by Karl
Pearson to find skewness in a
sample using descriptive statistics
like the mean and mode.

A

Pearson Coefficient of skewness

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31
Q

Symmetrical distribution and mode occur when the values of variables occur at regular frequencies and the mean, median at the same point

A

Symmetrical distribution

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32
Q

(or right-skewed) distribution is
a type of distribution in which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer

A

a positively skewed

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33
Q

is a type of distribution in which more values are concentrated on the right side (tail) of the distribution graph while the left tail of the distribution graph is longer.

A

Negatively skewed

34
Q

is a measure of whether the data are heavy- tailed or light-tailed relative to a normal distribution

A

Kurtosis

35
Q

data sets with high kurtosis

Data sets with low kurtosis

A

tend to have heavy
tails, or outliers

tend to have
light tails, or lack of outliers.

36
Q

indicates a positive excess
kurtosis. The leptokurtic distribution
shows heavy tails on either side,
indicating large outliers.

A

Leptokurtic

37
Q

shows a negative excess
kurtosis.

A

platykurtic distribution

38
Q

reveals a distribution with flat tails

A

kurtosis

39
Q

The characteristic of a frequency distribution that ascertains its symmetry about the mean is
called.

A

skewness

40
Q

means the relative pointedness of the standard bell curve, defined by the frequency distribution.

A

Kurtosis

41
Q

is a measure of the
degree of lopsidedness in the
frequency distribution.

A

Skewness

42
Q

is a measure of degree of
tailedness in the frequency
distribution

A

kurtosis

43
Q

is an indicator of lack of
symmetry, i.e. both left and right sides of the curve are unequal, with respect to the central point.

A

Skewness

44
Q

As against this,___ is a measure of data, that is either peaked or flat, with respect to the probability
distribution.

A

kurtosis

45
Q

Continuous probability distribution
* Uses interval or ratio level of
measur

A

Normal Distribution (z-distribution)

46
Q

Normal Distribution is a unique
arrangement of values in that if the
values are graphed, the curve takes a
distinct bell-shaped and symmetrical
form.

A

Normal Distributio

47
Q

CHARACTERISTICS OF NORMAL
DISTRIBUTION

A
  1. The curve is continuous.
  2. The curve is bell-shaped.
  3. The curve is symmetrical about the mean.
  4. The mean, median, and mode are located at the
    center of the distribution and are equal to each
    other.
  5. The curve is unimodal.
  6. The curve never touches the x-axis
    (asymptote).
  7. The total area under the normal curve is equal
    to 1.
48
Q

Is a point in the distribution such that is a given number of cases is below it.
Is a measure of relative standing.
It is a descriptive measure of the
relationship of a measurement to the rest of the data.

A

PERCENTILES

49
Q

He was an influential English mathematician
and biostatician.

A

Karl Pearson (1857-1936)

50
Q

It is a statistical method used to determine
whether a relationship between two variables
exists.
It also measure of the direction and strength of
linear relationship between two variables.
Direction maybe positive, negative or zero.

A

Correlation

51
Q

3 Types of Correlation

A

Positive correlation
Negative correlation
Zero correlation

52
Q

exists when high
values of one variable correspond to high values in the other variable or low values in one variable correspond to low values in the other variable.

A

positive correlation

53
Q

exist when high
values of one variable correspond to low
values in the other variable or low values in
one variable correspond to high values in the
other variable

A

negative correlation

54
Q

exists when high values
in one variable correspond to either high or
low values in the other variable.

A

zero correlation

55
Q

can be perfect, strong or high, moderate, low, zero or no correlation.

A

Strength

56
Q

A _ used to show how each point collected from a set of bivariate data are scattered on the Cartesian
plane

A

scatter plot (or scatter diagram) i

57
Q

It gives a good visual picture between the two
variables.
It is a graphical representation of the
relationship between two variables.

A

scatter plot

58
Q

The most widely used in statistics to measure the degree of the relationship between the linear
related variables.

A

Pearson Product-Moment Correlation

59
Q

The _ would require both variables to be normally distributed

A

Pearson r correlation

60
Q

observed the value against their frequency

A

histogram

61
Q

observed values against normally distributed data

A

q-q probability plots

62
Q

if the data is normally distributed, the points in a q-q plot will lie on a straight diagonal line

A

q-q probability plots

63
Q

are typically not very
useful when the sample size is small.

A

Graphical methods

64
Q

One of the most popular tests for normality assumption diagnostics which has good properties of power and is based on correlation within given observations and
associated normal scores

A

Shapiro-Wilk Test
(Numerical method)

65
Q

The sample data follows a normal distribution

A

Ha: alternative hypothesis.

66
Q

The sample data does not follow a normal distribution.

A

Ho: null hypothesis

67
Q

When we are testing normality

A
  • If P-value is greater than the alpha, it means that the data are normal.
  • If P-value is less than the alpha, it means that the data are NOT normal.
68
Q

correlation coefficient and strength of relationships

A

0.00 no correlation, no relationship

±0.01 to ±0.20 very low correlation, almost negligible relationship

±0.21 to ±0.40 slight correlation, definite but small relationship

±0.41 to ±0.70 moderate correlation, substantial relationship

±0.71 to ±0.90 high correlation, marked relationship

±0.91 to ±0.99 very high correlation, very dependable relationship

±1.00 perfect correlation, perfect relationship

69
Q

To identify if there is a significant
relationship between.

A
  • Step 1. State the null and alternative
    hypotheses.
  • Step 2. Determine the value of alpha.
  • Step 3. Identify the test statistics.
  • Step 4. Determine the degrees of freedom, computed t-value, and critical t-value.
  • Step 5. If the computed t is greater than or equal to the critical value of t then reject the null hypothesis.
    If the computed t is less than the critical value of t then accept the null hypothesis.
  • Step 6. Formulate your conclusion and interpretation
70
Q

if there is a significant relationship between two set of scores.

A

t-test

71
Q

This tells us how much of dependent variable () is due to or can be attributed to independent variable

This is denoted as r^2.

A

Coefficient of Determination

72
Q

is one type of fee paid for the use of money

A

Simple interest

73
Q

the percentage charged or earned

A

rate of interest

74
Q

the amount of money borrowed or invested

A

principal

75
Q

money is borrowed or invested (in years)

A

time

76
Q

amount formula

A

P + I = A

77
Q

formula for Rate of Interest

A

p+I=
after that, substitute from the both side

78
Q

It is calculated on the principal amount and also on the accumulated interest of previous periods, and can thus be regarded as “interest on interest”.

A

Compound Interest

79
Q

compounding frequency:

annually
semi annually
quarterly
monthly

A

number of compounding periods:

1
2
4
12

80
Q
A

BUHAY DENTISTA (16 decks)

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